In this paper, we have presented an approach based on fully convolutional neural networks for the super-resolution of CT/MRI scans. Our method is able to reliably upscale 3D CT/MRI image up to a scale factor of \(4\times \) . We have compared our approach with several baseline interpolation and state-of-the-art methods [1]}, [2]}, [3]}, [4]}. The empirical results indicated that our approach provides superior results on both CH and NAMIC data sets. We have also conducted a subjective image quality assessment by human observers, showing that our method is significantly better than Lanczos interpolation. The subjective image quality assessment also revealed the limitations of a pure algorithmic approach. The doctors invited to take part in our study concluded that going to a scale factor higher than \(4\times \)  requires alternative solutions. In future work, we aim to continue our research by extending the proposed CNN method to multi-channel input. This will likely help us in achieving higher upscaling factors, e.g. \(10\times \) , required for the accurate diagnostics and treatment of cancer, an actively studied and extremely important research topic [5]}, [6]}.
